Millions of ship containers are transported to and from the worlds' shipping ports every day. Accurate book-keeping of these containers is vital to ensure timely arrival and dispatch of goods for trade. Each container is granted a unique identification serial code, which is manually recorded when the container arrives at or leaves a port.
An automated system for reading of container codes from camera would be faster, cheaper and more reliable. However, automated reading and recording of container numbers at human performance levels has been a challenge due to the corrugated container surface, different background layouts, and variations in colors, font types, sizes, illumination, blur, orientations and other photometric distortions. The corrugated surface, in particular, implies that the character and font is distorted owing to a 2D projection from a 3D object, causing standard OCR techniques to perform poorly. Other challenges include rust on the container, mud, peeling paint and external factors such as varying lighting conditions, rain, fog, snow which affect the contrast of the grabbed vehicle or container images.
The performance of conventional image processing based methods for locating the container code regions which are recognized using a SVM classifier depends heavily on the positioning of the camera capturing the container image. Moreover, majority of prior art teaches methods and systems that use multiple modalities like vision and RFIDs for identification which have installation costs. The efficacy of existing methods for container code recognition where texts are printed on a corrugated surface still remains question. Additionally, none of these methods are capable of dynamically adapting to distortions like Spatial Transformation Network.